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A scalable multi‐resolution spatio‐temporal model for brain activation and connectivity in fMRI data

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  • Stefano Castruccio
  • Hernando Ombao
  • Marc G. Genton

Abstract

Functional Magnetic Resonance Imaging (fMRI) is a primary modality for studying brain activity. Modeling spatial dependence of imaging data at different spatial scales is one of the main challenges of contemporary neuroimaging, and it could allow for accurate testing for significance in neural activity. The high dimensionality of this type of data (on the order of hundreds of thousands of voxels) poses serious modeling challenges and considerable computational constraints. For the sake of feasibility, standard models typically reduce dimensionality by modeling covariance among regions of interest (ROIs)—coarser or larger spatial units—rather than among voxels. However, ignoring spatial dependence at different scales could drastically reduce our ability to detect activation patterns in the brain and hence produce misleading results. We introduce a multi‐resolution spatio‐temporal model and a computationally efficient methodology to estimate cognitive control related activation and whole‐brain connectivity. The proposed model allows for testing voxel‐specific activation while accounting for non‐stationary local spatial dependence within anatomically defined ROIs, as well as regional dependence (between‐ROIs). The model is used in a motor‐task fMRI study to investigate brain activation and connectivity patterns aimed at identifying associations between these patterns and regaining motor functionality following a stroke.

Suggested Citation

  • Stefano Castruccio & Hernando Ombao & Marc G. Genton, 2018. "A scalable multi‐resolution spatio‐temporal model for brain activation and connectivity in fMRI data," Biometrics, The International Biometric Society, vol. 74(3), pages 823-833, September.
    • Handle: RePEc:bla:biomet:v:74:y:2018:i:3:p:823-833
    DOI: 10.1111/biom.12844
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    References listed on IDEAS

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    2. Gordana Derado & F. DuBois Bowman & Clinton D. Kilts, 2010. "Modeling the Spatial and Temporal Dependence in fMRI Data," Biometrics, The International Biometric Society, vol. 66(3), pages 949-957, September.
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    5. Hakmook Kang & Hernando Ombao & Crystal Linkletter & Nicole Long & David Badre, 2012. "Spatio-Spectral Mixed-Effects Model for Functional Magnetic Resonance Imaging Data," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 107(498), pages 568-577, June.
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    Cited by:

    1. Edwards, Matthew & Castruccio, Stefano & Hammerling, Dorit, 2020. "Marginally parameterized spatio-temporal models and stepwise maximum likelihood estimation," Computational Statistics & Data Analysis, Elsevier, vol. 151(C).
    2. Felipe Tagle & Marc G. Genton & Andrew Yip & Suleiman Mostamandi & Georgiy Stenchikov & Stefano Castruccio, 2020. "Rejoinder to the discussion on A high‐resolution bilevel skew‐t stochastic generator for assessing Saudi Arabia's wind energy resources," Environmetrics, John Wiley & Sons, Ltd., vol. 31(7), November.
    3. Suprateek Kundu & Benjamin B. Risk, 2021. "Scalable Bayesian matrix normal graphical models for brain functional networks," Biometrics, The International Biometric Society, vol. 77(2), pages 439-450, June.
    4. Marc G. Genton & Ying Sun, 2019. "Comments on: Data science, big data and statistics," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 28(2), pages 338-341, June.

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